文章基本信息

标题：Reducing Excessive Amounts of Data: Multiple Web Queries for Generation of Pun Candidates
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作者：Pawel Dybala ; Michal Ptaszynski ; Kohichi Sayama 等
期刊名称：Advances in Artificial Intelligence
印刷版ISSN：1687-7470
电子版ISSN：1687-7489
出版年度：2011
卷号：2011
DOI：10.1155/2011/107310
出版社：Hindawi Publishing Corporation
摘要：Humor processing is still a less studied issue, both in NLP and AI. In this paper we contribute to this field. In our previous research we showed that adding a simple pun generator to a chatterbot can significantly improve its performance. The pun generator we used generated only puns based on words (not phrases). In this paper we introduce the next stage of the system's development—an algorithm allowing generation of phrasal pun candidates. We show that by using only the Internet (without any hand-made humor-oriented lexicons), it is possible to generate puns based on complex phrases. As the output list is often excessively long, we also propose a method for reducing the number of candidates by comparing two web-query-based rankings. The evaluation experiment showed that the system achieved an accuracy of 72.5% for finding proper candidates in general, and the reduction method allowed us to significantly shorten the candidates list. The parameters of the reduction algorithm are variable, so that the balance between the number of candidates and the quality of output can be manipulated according to needs.